了解不同类型的可重复性和类内相关性,用于分析生物变异
Shinichi Nakagawa1,2,3, David F Westneat1,4, Ayumi Mizuno1,2
1The Statistical Quantification of Individual Differences (SQuID) Working Group.
Journal of the Royal Society, Interface
|January 27, 2026
概括
可重复性,或类内相关性,量化了特征的变异来源. 本综述指导研究人员从混合效应模型中选择适当的方法来计算可重复性,并强调透明的报告.
科学领域:
- 生态学和进化生物学.
- 定量生物学 定量生物学
- 行为生态学 行为生态学
背景情况:
- 可重复性,也称为类内相关性,对于分解变异和识别生物特征变异来源至关重要.
- 线性混合效应模型和一般化的线性混合效应模型通常用于估计方差元件和可重复性.
- 了解重复性对于解释生物数据和得出有效结论至关重要.
研究的目的:
- 从混合效应模型中审查计算可重复性的不同方法.
- 根据特定的研究条件和问题,为选择最合适的可重复性变体提供指导.
- 强调推断范围的重要性以及对差异组件的透明报告.
主要方法:
- 使用混合效应模型对重复性估计现有文献的审查.
- 在不同的场景下分析各种可重复性计算变体.
- 专注于行为生物学和生态学的应用.
主要成果:
- 从混合效应模型中计算可重复性存在多种变体,每个变体适合不同的条件.
- 没有任何一种方法是普遍最佳的;选择取决于研究人员的问题和推断范围.
- 建议对所有差异组件进行透明报告,以获得准确的解释.
结论:
- 在选择可重复性方法时,研究人员应该仔细考虑他们的研究问题和推断范围.
- 统计方法和差异组件的清晰报告提高了可重复性估计的可靠性和可解释性.
- 本综述是经验学家在选择和解释可重复性指标时的实用指南.
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